Toward Cyborg: Exploring Long-Term Clinical Outcomes of a Multi-Degree-of-Freedom Myoelectric Prosthetic Hand

Abstract

Recent advancements in robotics and sensor technology have facilitated the development of myoelectric prosthetic hands (MPHs) featuring multiple degrees of freedom and heightened functionality, but their practical application has been limited. In response to this situation, formulating a control theory ensuring the hand dexterity of highly functional MPHs has garnered marked attention. Progress in this field has been directed toward employing machine-learning algorithms to process electromyogram patterns, enabling a broad spectrum of hand movements. In particular, the practical application of 5-finger-driven MPHs with such control functions to real users remains limited, and their attributes and challenges have not been thoroughly examined. In this study, we developed a 5-finger MPH equipped with pattern recognition capabilities. Through a long-term clinical trial, encompassing task assessments and subjective evaluations via questionnaires, we explored the MPH's range of applications. The task assessments revealed an expanded range of achievable tasks as the variety of motions increased. However, this enhanced adaptability was paralleled by a decrease in control reliability. Additionally, findings from the questionnaires indicated that enhancements in task performance with MPHs might be more effective in reducing workplace-related disability than in improving activities in everyday life. This study offers valuable insights into the long-term clinical prospects and constraints associated with multi-degree-of-freedom MPHs incorporating pattern recognition functionality.

Fig. 1.

Overview of the BIT-UEC-Hand. EMG, electromyogram; 3 ch, 3-channel.

Fig. 2.

(A and B) Screenshots of the tablet application. The Japanese-language indications relevant to this study have been translated into English and highlighted in red. (A) Mode check: confirmation and measurement of raw EMG signals are possible. The upper section of the screen contains buttons for selecting the microcontroller and establishing Bluetooth connectivity with it. The 3 waveforms displayed at the bottom are raw EMG data. Each waveform represents a raw EMG signal from channel 1 to channel 3 of the 3-channel EMG sensor. (B) Mode motion: allows the inspection of features on a radar chart, including the mean absolute value (MAV) of the EMG, labeling of teacher data, and recognition results. The radar chart is displayed in a circle at the lower left section, with 3 circular sector sections illustrating the values of the 3-channel feature data. The recognition result is indicated by a number (e.g., “1”) on the right side of the figure, corresponding to each motion. The value “3.020” in the lower right corner denotes the MAV, accompanied by the MAV waveform displayed in the lower right section. The upper part of the tablet screen contains buttons for labeling teacher data. (C) Classification of EMG signals.

Fig. 3.

(A) Experiment schedule. Day 0 (orange line) is the day that the participant started using the BIT-UEC-Hand in each household. The position of the arrow corresponds to the day on which the evaluation was performed. The text in the squares indicates the day and type of assessment conducted. (B) Items for the Upper Extremity Function Test (UEFT). DASH, Disability of the Arm, Shoulder, and Hand; BBT, Box and Block Test; Peg, Large Peg Pull Test.

Fig. 4.

(A) UEFT’s results. Notably, the number of motions used was different for each test. Four motions were evaluated when the * was on the pointer; otherwise, 3 motion controls were used. The fourth motion was a 3-finger pinch in all cases. Day 0 is the day that the participant started using the MPH in each household. (B) BBT’s results and Peg’s results. The fourth motion was a 2-finger pinch.

Fig. 5.

Tasks that could be performed with the UEFT. This table summarizes the actions that resulted in 2 or 3 points that were determined to be feasible by the UEFT. (A) Wooden cube (9 cm); (B) wooden cube (7 cm); (C) wooden cube (5 cm); (D) wooden cube (2.5 cm); (E) large iron pipe ($\varphi$ 38 mm); (F) small iron pipe ($\varphi$ 19 mm); (G) slate (2.5 cm × 1.2 cm × 11 cm); (H) wooden ball (7.5 cm); (I) dumbbell (3 kg, $\varphi$ 28 mm); (J) steel washer ($\varphi$ 78 mm × 2 mm); (K) glass marble ($\varphi$ 17 mm); (L) metal sphere ($\varphi$ 11 mm); pouring water from a glass into glass: (M) pronation and (N) supination; and (O) pouring water from a pitcher into a glass.

Fig. 6.

Results of the questionnaire regarding the degree of disability. “BIT” indicates the level of disability when wearing the BIT-UEC-Hand. “Without MPH” indicates the level of disability when not wearing the MPH. To facilitate comparison, the regression function of the “Participant A (BIT)” data is shown as a dotted line. Notably, higher values denote higher levels of disability. Day 0 is the day that the participant started using the MPH in each household. (A) Degree of disability in daily life. (B) Degree of disability at workplace.

Fig. 7.

Actual examples of participants working with the BIT-UEC-Hand. (A) Participant A harvests shiitake mushrooms with the unaffected hand while holding a mushroom bed with the BIT-UEC-Hand (Video S1). (B) Participant A grabs dried mushrooms with the BIT-UEC-Hand and places them in a bag (Video S2). (C) Participant A carries a case containing shiitake mushrooms using both hands (Video S3).

Fig. 8.

Each participant performing additional tasks. From top to bottom: Grasping a block in a narrow space, folding a page, folding a towel, and wiping a desk with a towel.